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Record W2972597576 · doi:10.1177/2514848619871047

Encountering the burn: Prescribed burns as contact zones

2019· article· en· W2972597576 on OpenAlexaffabout
Colin Robert Sutherland

Bibliographic record

VenueEnvironment and Planning E Nature and Space · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsYork University
Fundersnot available
KeywordsPrescribed burnFire regimeWildfire suppressionFire protectionNational parkScale (ratio)Embodied cognitionEnvironmental resource managementGeographyEcosystemHistoryEnvironmental ethicsEnvironmental planningEcologyEngineeringArchaeologyCivil engineeringEnvironmental scienceForestryCartography

Abstract

fetched live from OpenAlex

Encounters with fire and landscapes that burn have the potential to be both disastrous and life-giving events. In Canadian national parks, where a century of fire suppression has ruled human encounters with fire adapted landscapes, fire managers and ecologists are eagerly returning fire to diverse ecosystems in the hopes of building healthier ecosystems and reducing the risk of larger wildfire events. Ongoing changes to park policy have made new relationships with fire possible on these federal lands. Prescribed burns, whereby fire is applied to the landscape by park managers, is one such emerging encounter made possible by these policy changes. By reconceptualizing the burn as a process constituted by encounters, in what Mary Louise Pratt would call a contact zone, we gain insight into how thinking and working with fire requires an attention to how humans and more-than-humans encounter one another and the institutional settings which narrate and often constrain these encounters. In the case of Parks Canada’s fire program, this tool of active management, and an alternative to full-suppression, illustrates how thinking and working with fire consists of a set of encounters which take place at both an institutional and embodied scale.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.268
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2019
Admission routes2
Has abstractyes

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